Towards a heterogeneous mist, fog, and cloud based framework for the Internet of Healthcare Things

Rapid developments in the fields of information and communication technology and microelectronics allowed seamless interconnection among various devices letting them to communicate with each other. This technological integration opened up new possibilities in many disciplines including healthcare and well-being. With the aim of reducing healthcare costs and providing improved and reliable services, several healthcare frameworks based on Internet of Healthcare Things (IoHT) have been developed. However, due to the critical and heterogeneous nature of healthcare data, maintaining high quality of service (QoS) -in terms of faster responsiveness and data-specific complex analytics -has always been the main challenge in designing such systems. Addressing these issues, this paper proposes a five-layered heterogeneous mist, fog, and cloud based IoHT framework capable of efficiently handling and routing (near-)real-time as well as offline/batch mode data. Also, by employing software defined networking and link adaptation based load balancing, the framework ensures optimal resource allocation and efficient resource utilization. The results, obtained by simulating the framework, indicate that the designed network via its various components can achieve high QoS, with reduced end-to-end latency and packet drop rate, which is essential for developing next generation e-healthcare systems

A Safe, Efficient and Integrated Indoor Robotic Fleet for Logistic Applications in Healthcare and Commercial Spaces: The ENDORSE Concept

International audienceHospitals are rightfully considered a field of indoor logistic robotics of high commercial potential. However, today, only a handful of mobile robotic solutions for hospital logistics exist that have failed to trigger widespread acceptance by the market. This is because existing systems require costly infrastructure installation, they do not easily integrate to corporate IT solutions, are not adequately shielded from cybersecurity threats, and as a result, they do not fully automate procedures and traceability of the items they carry. Moreover, existing systems are limited on scope, focusing only on delivery services, and hence do not provide any other type of support to the medical and nursing staff. ENDORSE system will address the aforementioned technical challenges and functional limitations by pursuing four innovation pillars: (i) infrastructure-less multi-robot indoor navigation; (ii) advanced Human-Robot Interaction (HRI) for resolving deadlocks and achieving efficient sharing of space resources in crowded environments; (iii) deployment of the ENDORSE software as a cloud-based service facilitating its integration with corporate software solutions, complying with GDPR data security requirements; (iv) reconfigurable and modular hardware architectures so that diverse modules can be easily swapped. ENDORSE functionality will be demonstrated via the integration of an e-diagnostic support module for vital signs monitoring on a fleet of mobile robots, facilitating connectivity to cloud-based Electronic Health Records (EHR), and validated in an operational hospital environment for realistic assessment

The use of extended reality and machine learning to improve healthcare and promote greenhealth

Com a Quarta Revolução Industrial, a propagação da Internet das Coisas, o avanço nas áreas de Inteligência Artificial e de Machine Learning até à migração para a Computação em Nuvem, o termo "Ambientes Inteligentes" cada vez mais deixa de ser uma idealização para se tornar realidade. Da mesma forma as tecnologias de Realidade Extendida também elas têm aumentado a sua presença no mundo tecnológico após um "período de hibernação", desde a popularização do conceito de Metaverse assim como a entrada das grandes empresas informáticas como a Apple e a Google num mercado onde a Realidade Virtual, Realidade Aumentada e Realidade Mista eram dominadas por empresas com menos experiência no desenvolvimento de sistemas (e.g. Meta), reconhecimento a nível mundial (e.g. HTC Vive), ou suporte financeiro e confiança do mercado. Esta tese tem como foco o estudo do potencial uso das tecnologias de Realidade Estendida de forma a promover Saúde Verde assim como seu uso em Hospitais Inteligentes, uma das variantes de Ambientes Inteligentes, incorporando Machine Learning e Computer Vision, como ferramenta de suporte e de melhoria de cuidados de saúde, tanto do ponto de vista do profissional de saúde como do paciente, através duma revisão literarária e análise da atualidade. Resultando na elaboração de um modelo conceptual com a sugestão de tecnologias a poderem ser usadas para alcançar esse cenário selecionadas pelo seu potencial, sendo posteriormente descrito o desenvolvimento de protótipos de partes do modelo conceptual para Óculos de Realidade Extendida como validação de conceito.With the Fourth Industrial Revolution, the spread of the Internet of Things, the advance in the areas of Artificial Intelligence and Machine Learning until the migration to Cloud Computing, the term "Intelligent Environments" increasingly ceases to be an idealization to become reality. Likewise, Extended Reality technologies have also increased their presence in the technological world after a "hibernation period", since the popularization of the Metaverse concept, as well as the entry of large computer companies such as Apple and Google into a market where Virtual Reality, Augmented Reality and Mixed Reality were dominated by companies with less experience in system development (e.g. Meta), worldwide recognition (e.g. HTC Vive) or financial support and trust in the market. This thesis focuses on the study of the potential use of Extended Reality technologies in order to promote GreenHealth as well as their use in Smart Hospitals, one of the variants of Smart Environments, incorporating Machine Learning and Computer Vision, as a tool to support and improve healthcare, both from the point of view of the health professional and the patient, through a literature review and analysis of the current situation. Resulting in the elaboration of a conceptual model with the suggestion of technologies that can be used to achieve this scenario selected for their potential, and then the development of prototypes of parts of the conceptual model for Extended Reality Headsets as concept validation

The development and implementation of e-health services for the Libyan NHS: case studies of hospitals and clinics in both urban and rural areas

This thesis provides an assessment of the readiness levels within both urban and rural hospitals and clinics in Libya for the implementation of E-health systems. This then enabled the construction of a framework for E-health implementation in the Libyan National Health Service (LNHS). The E-health readiness study assessed how medications were prescribed, how patients were referred, how information communication technology (ICT) was utilised in recording patient records, how healthcare staff were trained to use ICT, and how the ways in which consultations were carried out by healthcare staff. The research was done in five rural clinics and five urban medical centres and focused on the E-health readiness levels of the technology, social attitudes, engagement levels and any other needs that were apparent. Collection of the data was carried out using a mixed methods approach with qualitative interviews and quantitative questionnaires. The study indicated that any IT equipment present was not being utilised for clinical purposes and there was no evidence of any E-health technologies being employed. This implies that the maturity level of the healthcare institutions studied was at level zero in the E-health maturity model used in this thesis. In order for the LNHS to raise its maturity levels for the implementation of E-health systems, it needs to persuade LNHS staff and patients to adopt E-health systems. This can be carried out at a local level throughout the LNHS, though this will need to be coordinated at a national level through training, education and programmes to encourage compliance and providing incentives. In order to move E-health technology usage in the participating Libyan healthcare institutions from Level 0 to Level 2 in the E-health Maturity Model levels, an E-health framework was created that is based on the findings of this research study. The primary aim of the LNHS E-Health Framework is the integration of E-health services for improving the delivery of healthcare within the LNHS. To construct the framework and ensure that it was creditable and applicable, work on it was informed directly by the findings from document analysis, literature review, and expert feedback, in conjunction with the primary research findings presented in Chapter Five. When the LNHS E-Health Framework was compiled there were several things taken into consideration, such as: the abilities of healthcare staff, the needs of healthcare institutions and the existing ICT infrastructure that had been recorded in the E-readiness assessment which was carried out in the healthcare institutions (Chapter 5). The framework also provides proposals for E-health systems based on the infrastructure network that will be developed. The processes addressed are electronic health records, E-consultations, E-prescriptions, E-referrals and E-training. The researcher has received very positive, even enthusiastic, feedback from the LNHS and other officals, and that expect the framework to be further developed and implemented by the LNHS in the near future

Towards fostering the role of 5G networks in the field of digital health

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future

Réseaux de capteurs ubiquitous dans l'environnement NGN

Ubiquités Sensor Network (USN) is a conceptual network built over existing physical networks. It makes use of sensed data and provides knowledge services to anyone, anywhere and at anytime, and where the information is generated by using context awareness. Smart wearable devices and USNs are emerging rapidly providing many reliable services facilitating people life. Those very useful small end terminals and devices require a global communication substrate to provide a comprehensive global end user service. In 2010, the ITU-T provided the requirements to support USN applications and services in the Next Génération Network (NGN) environment to exploit the advantages of the core network. One of the main promising markets for the USN application and services is the e-Health. It provides continuous patients’ monitoring and enables a great improvement in medical services. On the other hand, Vehicular Ad-Hoc NETwork (VANET) is an emerging technology, which provides intelligent communication between mobile vehicles. Integrating VANET with USN has a great potential to improve road safety and traffic efficiency. Most VANET applications are applied in real time and they are sensitive to delay, especially those related to safety and health. In this work, we propose to use IP Multimedia Subsystem (IMS) as a service controller sub-layer in the USN environment providing a global substrate for a comprehensive end-to-end service. Moreover, we propose to integrate VANETs with USN for more rich applications and facilities, which will ease the life of humans. We started studying the challenges on the road to achieve this goalUbiquitous Sensor Network (USN) est un réseau conceptuel construit sur des réseaux physiques existantes. Il se sert des données détectées et fournit des services de connaissances à quiconque, n'importe où et à tout moment, et où l'information est générée en utilisant la sensibilité au contexte. Dispositifs et USN portables intelligents émergent rapidement en offrant de nombreux services fiables facilitant la vie des gens. Ces petits terminaux et terminaux très utiles besoin d'un substrat de communication globale pour fournir un service complet de l'utilisateur final global. En 2010, ITU -T a fourni les exigences pour supporter des applications et services USN dans le Next Generation Network (NGN) de l'environnement d'exploiter les avantages du réseau de base. L'un des principaux marchés prometteurs pour l'application et les services USN est la e- santé. Il fournit le suivi des patients en continu et permet une grande amélioration dans les services médicaux. D'autre part, des Véhicules Ad-hoc NETwork (VANET) est une technologie émergente qui permet une communication intelligente entre les véhicules mobiles. Intégrer VANET avec USN a un grand potentiel pour améliorer la sécurité routière et la fluidité du trafic. La plupart des applications VANET sont appliqués en temps réel et ils sont sensibles à retarder, en particulier ceux liés à la sécurité et à la santé. Dans ce travail, nous proposons d'utiliser l'IP Multimédia Subsystem (IMS) comme une sous- couche de contrôle de service dans l'environnement USN fournir un substrat mondiale pour un service complet de bout en bout. De plus, nous vous proposons d'intégrer VANETs avec USN pour des applications et des installations riches plus, ce qui facilitera la vie des humains. Nous avons commencé à étudier les défis sur la route pour atteindre cet objecti

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Design and Mining of Health Information Systems for Process and Patient Care Improvement

abstract: In healthcare facilities, health information systems (HISs) are used to serve different purposes. The radiology department adopts multiple HISs in managing their operations and patient care. In general, the HISs that touch radiology fall into two categories: tracking HISs and archive HISs. Electronic Health Records (EHR) is a typical tracking HIS, which tracks the care each patient receives at multiple encounters and facilities. Archive HISs are typically specialized databases to store large-size data collected as part of the patient care. A typical example of an archive HIS is the Picture Archive and Communication System (PACS), which provides economical storage and convenient access to diagnostic images from multiple modalities. How to integrate such HISs and best utilize their data remains a challenging problem due to the disparity of HISs as well as high-dimensionality and heterogeneity of the data. My PhD dissertation research includes three inter-connected and integrated topics and focuses on designing integrated HISs and further developing statistical models and machine learning algorithms for process and patient care improvement. Topic 1: Design of super-HIS and tracking of quality of care (QoC). My research developed an information technology that integrates multiple HISs in radiology, and proposed QoC metrics defined upon the data that measure various dimensions of care. The DDD assisted the clinical practices and enabled an effective intervention for reducing lengthy radiologist turnaround times for patients. Topic 2: Monitoring and change detection of QoC data streams for process improvement. With the super-HIS in place, high-dimensional data streams of QoC metrics are generated. I developed a statistical model for monitoring high- dimensional data streams that integrated Singular Vector Decomposition (SVD) and process control. The algorithm was applied to QoC metrics data, and additionally extended to another application of monitoring traffic data in communication networks. Topic 3: Deep transfer learning of archive HIS data for computer-aided diagnosis (CAD). The novelty of the CAD system is the development of a deep transfer learning algorithm that combines the ideas of transfer learning and multi- modality image integration under the deep learning framework. Our system achieved high accuracy in breast cancer diagnosis compared with conventional machine learning algorithms.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Secondary Analysis of Electronic Health Records

Health Informatics; Ethics; Data Mining and Knowledge Discovery; Statistics for Life Sciences, Medicine, Health Science

A grey approach to predicting healthcare performance

© 2018 Elsevier Ltd The success of an organization or a particular activity is evaluated through the measurement of key performance indicators (KPIs). The aim of this paper is to analyze and predict the indicators of healthcare performance using grey systems theory. Recent advancements in science and technology have made the healthcare industry extremely efficient at collecting data using electronic claims systems such as electronic health records. Therefore, collecting field level primary data becomes easier and accumulate them to generate secondary data for research purpose and to get an insight of the organization performance is absolutely necessary. Our research analyzes the KPIs of a hospital based on a secondary data source. Since, secondary data contains uncertainty and sometimes poor information, grey prediction model suits best to make a prediction model in this regard. Conventional grey model has considerable drawbacks while making a rigorous prediction model. For this, we apply an improved grey prediction model to predict the KPIs of the healthcare performance indicators. Several error measures in our model give a best fit of the data and allow prediction of the KPIs. The prediction model gives good estimates of the quantitative indicators and produced error rate within an acceptable range. We observe that the KPIs of bed turnover rate (BTR) and bed occupancy rate (BOR) have an increasing trend, whereas the KPIs of average length of stay (ALOS), hospital death rate (HDR) and hospital infection rate (HIR) show a decreasing trend over time. The main contribution of this research is a grey-based prediction model that can provide managers with the information they need to evaluate and predict the performance of a hospital. The research indicates that managers should give greater priority to the indicators which will result in better patients’ satisfaction and improved profit margin. Healthcare managers striving towards better performance will now have an empirical basis upon which to formulate and adjust their strategies, after analyzing the predicted value